490 results about "Heat transfer model" patented technology

The invention provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention discloses a method and a system for predicting a temperature field of a high-capacity energy storage lithium ion battery. The method comprises the following steps: acquiring temperature values of a few temperature measuring points of the lithium ion battery to be predicted; inputting the obtained temperature value into a preset physical information-neural network model for prediction to obtain a temperature field of the lithium ion battery cell to be predicted; wherein the preset physical information-neural network model comprises a basic neural network part and a physical information fusion part, and the physical information fusion part adds a battery heat transfer differential equation and a battery boundary condition as physical information constraints in a neural network training process; the loss function in the neural network comprises boundary loss, partial differential equation loss and observation data set loss. The system comprises a data acquisition module and a data processing module. According to the method, the neural network algorithm is combined with the lithium battery heat transfer model, and temperature field inversion of the high-capacity battery is realized.

The invention relates to the technical field of industrial control, and discloses a dry-wet combined cooling tower operation state prediction and diagnosis system comprising a load prediction module used for obtaining production plan data and environment information, constructing a basic heat load prediction model, and outputting a total prediction heat load; the thermal modeling module is used for establishing a heat transfer model and an energy consumption model, the heat transfer model outputs cooling amounts in different operation modes, and the energy consumption model outputs total predicted power; the dry-wet decision module is used for constructing a multi-objective optimization function and solving the multi-objective optimization function to obtain a dry-wet switching strategy; and the control execution module is used for executing the dry-wet switching strategy. According to the method, the basic thermal load prediction model is established, so that the prediction precision of the thermal load is improved, and differentiated cooling strategies are provided for different production stages; on the basis of real-time load requirements and environmental conditions, a dry mode or a wet mode is intelligently selected for operation, and a multi-objective optimization function is constructed, so that the balance between cooling requirement meeting and system energy consumption minimization is realized.

The invention discloses a bottom heat flow and deep layer temperature inversion method based on an underground medium heat transfer model, and relates to the field of deep stratum temperature field prediction. The underground medium heat transfer model-based bottom heat flow and deep temperature inversion method mainly comprises the following steps of: constructing an electromagnetic data three-dimensional axial anisotropy inversion parameter target functional to carry out fine inversion on an electromagnetic data electrical structure of a target area to obtain an electrical structure model and an underground heat storage stratum structure model of the target area; a deep stratum heat transfer model is obtained in combination with regional geothermal geological information; and utilizing a Newton-Raphson optimization algorithm to perform inversion on the bottom prior heat flow value distribution boundary condition to obtain temperature field distribution of the deep stratum heat transfer model. According to the bottom heat flow and deep layer temperature inversion method based on the underground medium heat transfer model, the electrical characteristics of the underground medium can be converted into temperature field distribution based on a small number of borehole logging constraints, the prediction precision is high, and the prediction range is wide.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention provides a pile foundation buried pipe design optimization method, medium and system, and belongs to the technical field of ground source heat pump pile foundation buried pipe, and the method comprises the following steps: building a geologic model and a vibration parameter matrix by obtaining actually measured geology and vibration data of a to-be-placed pile foundation area; and a feature matching technology is utilized to find optimal matching in a preset typical soil database, clustering analysis is carried out, and the soil deformation amount of the region is determined. And in combination with load and vibration information of the pile foundation and the building, soil deformation in the whole using process is predicted. On the basis, an underground heat transfer model of the pile foundation buried pipe is established, and the heat exchange capacity of the ground source heat pump is calculated; meanwhile, the influence of the buried pipe on soil deformation is analyzed. And finally, the heat exchange capacity and soil deformation change serve as optimization targets, a game model is adopted for solving, and an optimal pile foundation buried pipe design scheme is obtained. In the whole process, the mode of combining measured data and modeling analysis is fully utilized, and intelligent optimization of pile foundation design is achieved.

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention discloses a heat transfer simulation method for a material freezing process, which comprises the following steps: step 1, according to each physical property parameter of a material before freezing, obtaining a liquid phase volume fraction VL of the material before freezing, a volume fraction VSL of a porous matrix before freezing of the material, an effective physical property parameter of a liquid phase before freezing of the material and an effective physical property parameter of the porous matrix before freezing of the material; step 2, obtaining the volume fraction VF of a frozen phase of the frozen material, the volume fraction VSF of a porous matrix of the frozen material, the effective physical property parameters of the frozen phase of the frozen material and the effective physical property parameters of the porous matrix of the frozen material according to the physical property parameters of the frozen material; and step 3, constructing a heat transfer model of the material freezing process according to the parameters in the step 1 and the step 2. According to the method, the temperature, phase change, freezing end point and the like in the freezing process can be accurately predicted, and standardization of freeze drying production of materials (especially agricultural products) is achieved.

The invention discloses an adaptive gradient enhanced Kriging agent modeling method suitable for spacecraft heat transfer model correction, and relates to the field of spacecraft heat transfer model correction. The method specifically comprises the following steps: S1, carrying out theoretical derivation construction on a related core function in a self-adaptive gradient enhancement modeling process of a gradient weighted Gaussian process prediction function and a target sample self-adaptive learning function Kriging; and S2, establishing a novel adaptive gradient enhanced Kriging proxy model of the spacecraft heat transfer model through a numerical method based on a proxy model related core function constructed by theoretical derivation in the step S1. The operation complexity that an adjoint function and a Taylor equation need to be solved in traditional gradient enhancement agent modeling is avoided, the gradient enhancement method is embedded into the iteration process of adaptive learning, and efficiency and accuracy are improved.

The invention provides a lithium ion battery core temperature prediction method based on a finite element model, and the method comprises the following steps: 1, designing an electrochemical test experiment, and constructing a one-dimensional electrochemical model containing a lithium ion battery electrochemical heat production mechanism; 2, designing a lithium ion battery temperature test experiment, obtaining electrochemical parameters, heat transfer characteristic parameters, internal thermophysical parameters and environmental parameters of the battery under different working conditions, and obtaining a temperature change curve of the center position and the surface of the battery; and step 3, based on the heat balance equation, establishing a three-dimensional heat transfer model having the same geometric characteristics as the battery used in the experiment. According to the experimental result in the step 2, the three-dimensional heat transfer model is subjected to non-uniform region division, each region corresponds to one one-dimensional electrochemical model, and the heat production rate per unit volume is calculated by the one-dimensional electrochemical model in the step 1; introducing the heat production rate of each area into a three-dimensional heat transfer model to calculate the temperature distribution of the battery, and designing an experiment to correct a one-dimensional electrochemical model; 4, modifying the operation conditions and heat dissipation conditions of the battery, and calculating and analyzing the temperature difference delta T between the center position and the surface position of the battery under different operation conditions and heat dissipation conditions by using the three-dimensional heat transfer model in the step 3; and 5, measuring the surface temperature of the battery, and calculating the temperature of the center of the battery according to the working condition of the battery in the step 4 and the delta T corresponding to the environment temperature, thereby realizing monitoring of the temperature of the center position of the battery. According to the method, a high-precision algorithm model is constructed and verified by utilizing data accumulated in an earlier-stage experiment, the central point temperature which is difficult to directly measure in the battery is predicted only through real-time and easily-acquired battery surface temperature information, operation condition parameters and heat dissipation conditions, key thermal state information is provided for a battery management system, and the battery management efficiency is improved. The method is used for real-time safety monitoring and thermal management optimization.

The invention discloses a lithium battery thermal parameter online monitoring method and equipment. According to the method, a one-dimensional heat transfer model of the battery is established, and a finite element model is solved by inversion of battery surface temperature change actually measured in operation, so that combined monitoring of the heat conductivity coefficient, the convective heat transfer coefficient, the average temperature and the heat production power P of the lithium battery is realized. Meanwhile, only the temperature sensor needs to be arranged on the surface of the battery, the battery does not need to be disassembled or an internal sensor does not need to be implanted, the battery integrity is kept, and the method is suitable for battery full-life-cycle monitoring. According to the method, the key thermal parameters of the lithium battery can be identified online in a non-intrusive, low-cost and high-reliability manner, support is provided for thermal management, health assessment and thermal safety early warning of the battery, and the method is suitable for various battery configurations and thermal management systems.

The invention discloses a rock-soil temperature field rapid solving method suitable for a ground source heat pump system, and the method comprises the steps: firstly building a three-dimensional unsteady-state heat transfer model in consideration of a buried pipe coaxial structure, a porous arrangement form and the characteristics of a multilayer heterogeneous soil body in the ground source heat pump system; then simulating a thermal response process by using the model, constructing a multi-domain coupled thermal conduction augmentation matrix, performing POD on the matrix, extracting a dominant mode function of a temperature field, and constructing a low-dimensional POD-based mode space; and finally, constructing a POD-Galerkin low-order model, simulating a buried pipe group soil temperature field, performing inversion of a heat source function, and outputting stratum thermophysical parameters. According to the method, the calculation efficiency and the simulation precision of the porous coupling and long-period unsteady state heat transfer process are remarkably improved, the problem that traditional finite element and finite difference methods are high in time consumption in porous strong coupling modeling is solved, and rapid prediction of the soil temperature and robust recognition of the heat source function under a complex stratum and a heat exchange structure can be achieved.

The invention belongs to the technical field of ocean engineering and water electrolysis hydrogen production. In order to solve the problems that a traditional water cooling system is large in size and low in heat dissipation efficiency, a multi-scale thermal sensing network is deployed to collect data; establishing a multi-phase-change cross-scale heat transfer model; constructing a two-stage gradient composite cooling module; constructing and optimizing a cooling decision algorithm; designing a cooperative control loop; deep reinforcement learning control is implemented; creating a functional surface; and constructing a dynamic optimization system. The characteristics of part of the steps are limited, efficient heat management can be achieved, and stable operation of the floating type offshore hydrogen production platform cooling system is guaranteed.

The invention relates to the technical field of infrared thermal imaging, in particular to a human body analysis method and system based on an infrared thermal image. The method comprises the following steps: acquiring an infrared thermal image sequence of a limb to be detected in a thermal relaxation process; segmenting the image sequence, constructing a topological center line of an effective contour, and defining a heat flow reference direction based on the geometric width change of a cross section along the center line; a temperature gradient vector field is calculated, and an area with the gradient direction and the heat flow reference direction in reverse distribution and containing a closed isotherm is screened out to serve as a thermal topology abnormal area; establishing a biological heat transfer model taking the surface temperature as a boundary condition, performing inverse solution on the thermal topology abnormal region, and performing inverse calculation on a heat source parameter corresponding to the subcutaneous depth; and when the heat source parameter exceeds a threshold value, generating a damage early warning. According to the method, by quantifying metabolic heat abnormality caused by inflammation in tissues, accurate positioning and quantitative evaluation of deep injury of limbs are realized, and the accuracy of human injury detection is remarkably improved.

The invention provides a high-strength aluminum alloy selective laser melting forming thermal stress prediction method based on deep learning. The method comprises the steps of 1, finite element model establishment and simulation, wherein a heat transfer model and a thermal coupling model are established; an SLM process is used as a simulation object, a Gaussian model is adopted to define a laser heat source, and thermal stress distribution under different process parameters and different sample sizes is simulated; wherein the process parameters comprise laser power, scanning speed and hatch spacing; step 2, data processing and deep learning model training; comprising the steps of data preprocessing, neural network model architecture determination, adversarial network part generation and model configuration and training. 3, evaluating and optimizing the model; and 4, thermal stress prediction and process optimization. By learning a complex mode in finite element simulation data through a deep learning model, efficient and accurate thermal stress prediction is achieved, and the problems that traditional finite element analysis is complex in calculation and large in resource consumption are solved.

The invention discloses a heat exchange station circulating pump characteristic adaptive optimization control method and system, and relates to the technical field of circulating pump control, and the method comprises the steps: building a building terminal heat transfer model and a building energy balance model based on the temperature distribution of the surface of a radiator, and then building a building load prediction model; performing numerical modeling on the performance curve of the heat exchange station circulating pump, and establishing a circulating pump performance model; based on a depth deterministic strategy gradient algorithm, establishing a neural network control model for self-adaptive control of the circulating pump of the heat exchange station; establishing a state, an action and a reward function of an agent of the neural network control model; and training the neural network control model to obtain a trained control model, and performing real-time control on the heat exchange station circulating pump based on the trained control model. The technical problems that a traditional control method of the circulating pump is low in operation efficiency and serious in energy waste are solved.

The invention discloses an energy shaft heat exchange efficiency optimization method based on stratum thermophysical property intelligent analysis, and the method comprises the steps: constructing a structured thermophysical property parameter database through integrating laboratory testing, on-site exploration and literature data, and covering key parameters such as a heat conductivity coefficient, specific heat capacity, a thermal diffusion coefficient and the like; a heat transfer model is verified and expanded by combining a reduced scale physical experiment and multi-parameter coupling numerical simulation; machine learning algorithms such as a random forest and a neural network are utilized to establish a nonlinear prediction model of the thermophysical parameters and the heat exchange efficiency of the energy shaft, the contribution degree of the parameters is quantified, and an adaptability threshold value is defined. Through similarity matching of drilling exploration data and a database, the optimal stratum depth is recommended, and finally a cloud intelligent platform is integrated, so that real-time data updating, online calculation and construction suggestions are realized. According to the method, the precision of stratum suitability evaluation in energy shaft ground source heat pump system development is remarkably improved, and the geothermal energy development efficiency is remarkably improved.

The invention discloses a heat transfer calculation method for an interval capillary core pulsating heat pipe, which comprises the following steps of: constructing a relational expression of steam bomb temperature and pressure, and calculating the steam bomb pressure; constructing a liquid bomb energy equation, and calculating a liquid bomb temperature initial value; constructing an interval capillary core pulsating heat pipe liquid elastic momentum equation, and calculating liquid elastic displacement; constructing different pipe section liquid film length calculation formulas, and calculating different pipe section liquid film lengths; the heat transfer amount of liquid film evaporation and condensation is obtained, and the corresponding mass variation of the steam bomb is calculated; obtaining a relational expression of mass flow and pressure of the steam bomb, and calculating to obtain a new pressure value of the steam bomb; constructing a relational expression between the temperature and the pressure of the steam bomb, and obtaining a new value of the temperature of the steam bomb; constructing a liquid bomb energy equation, obtaining a new liquid bomb temperature value, and carrying out comparison iteration; obtaining sensible heat transferred in and out of the liquid bomb, and calculating the thermal resistance of the pulsating heat pipe; and outputting the calculation result to complete the calculation of the heat transfer model. The method provides guidance for design and development of the pulsating heat pipe covering the capillary cores at intervals.

The invention relates to the technical field of intelligent water affairs, and discloses a dynamic monitoring method and system for the thickness of urban drainage pipeline sediment based on temperature monitoring, and the method comprises the steps: building a simulation device of an urban drainage pipeline sediment distribution scene; acquiring actual temperature time sequence data of sewage and sediments in the urban drainage pipeline; establishing a one-dimensional heat transfer model according to the actual temperature time sequence data, and calibrating model parameters in the one-dimensional heat transfer model; performing numerical simulation in the one-dimensional heat transfer model to obtain a simulation temperature time sequence of the sewage and the sediment; a local maximum / minimum value method and a dynamic tuning and harmonic regression method are adopted to analyze the periodic characteristics of the simulated temperature time sequence, and temperature time sequence characteristics are obtained; and developing a data-driven model, and associating the sediment thickness with the temperature time sequence characteristics. Through temperature monitoring and model analysis, the accuracy and efficiency of sediment monitoring are improved.

According to the temperature value calculation method, the air conditioner control method and the air conditioner, the temperature output function built in an intelligent power module is utilized, and the characteristic that the intelligent power module and a power factor correction module in an air conditioner driving board have the same or similar heat transfer structures is combined; and establishing a temperature calculation method based on the heat transfer model. The temperature (third temperature value) of the intelligent power module is calculated by obtaining the environment temperature (second temperature value) and the power value of the intelligent power module, and the accuracy of the heat transfer model is verified according to the temperature (third temperature value). And under the condition that the model verification is passed, further combining the power value of the power factor correction module and the environment temperature, and calculating the temperature (fourth temperature value) of the power factor correction module through a heat transfer model of the power factor correction module. According to the method, the temperature of the power device of the power factor correction module in the air conditioner driving board can be accurately calculated on the premise of not adding an additional temperature sensor.

The invention relates to a method for predicting a fluid temperature field of a middle-deep layer sleeve type buried pipe containing a horizontal well, and the method comprises the following steps: constructing a heat transfer model of the middle-deep layer sleeve type buried pipe containing the horizontal well, and dividing the model into a vertical section and a horizontal section; and carrying out iterative solution on the heat transfer model. Compared with the prior art, the method has the advantages that the calculation amount is reduced, the temperature field is accurately predicted, and the like.

The invention discloses a method for constructing a heat transfer model of a buried pipe heat exchanger and determining the hole depth of the buried pipe heat exchanger, and aims to solve the technical problem that the drilling depth of the current buried pipe heat exchanger is unreasonable. According to the technical scheme, based on the heat transfer model of the ground heat exchanger, the interval approximation method is adopted, the better burial depth range is gradually refined, so that the optimal arrangement hole depth within the specific depth range is found, the method is simple and scientific, the accuracy of predicting the performance of the ground heat exchanger can be improved, and then the efficiency and reliability of a ground source heat pump system are improved.

The present application relates to the field of fiber-reinforced resin matrix composite molding, and in particular to a thermal compensation design and implementation method for the near-net-shape molding of a thermoset composite component and based on a temperature field and a cure degree field. The method comprises: establishing a minimum symmetric unit model on the basis of the size of a component, the size of a mold, and an ambient temperature; establishing a cure kinetics model and a heat transfer model; incorporating a molding temperature profile into a subprogram; on the basis of an obtained temperature field and an obtained cure degree field, performing simulated thermal compensation on regions where the temperature is lower than a molding temperature and the temperature difference from the molding temperature is greater than or equal to 5% of the molding temperature, until the difference between the temperature of each region and the current molding temperature is lower than 5% of the molding temperature; then, performing stimulated thermal compensation on regions where the cure degree is less than or equal to 0.9; and assembling a thermal compensation apparatus on the basis of a thermal compensation design scheme, performing experimental validation, and when the difference between an actually measured temperature value and a simulated value is less than 5% and the cure degree exceeds 0.9, obtaining a thermal compensation scheme and a thermal compensation method. The blindness of traditional molding that relies on experience is reduced.

The invention relates to the technical field of power system demand side management and optimization operation, and discloses an electric heating load master-slave game collaborative optimization method based on AP clustering and adjustable potential modeling, which comprises the following steps: establishing a directly-heated electric heating indoor heat transfer model to analyze a single user to obtain load characteristics of heating users; a decentralized electric heating load refined aggregation model is constructed, and decentralized heating users are divided into different clusters; an adjustable potential quantification model is constructed, and the adjustable electric quantity and the adjustable potential of each cluster are obtained; and a heating load group optimization operation model based on the master-slave game is constructed, power grid purchase and sale electricity price information, distributed power supply cost information, adjustable electric quantity and adjustable potential are input, and a power utilization behavior adjustment strategy is obtained. According to the method, through refined house type clustering, master-slave game framework design and an improved solution algorithm, precise mining and optimized operation of the heating load group regulation potential are realized, the peak regulation pressure of a power grid is effectively reduced, and the comfort of a user is considered.

The invention discloses a thermal wave detection method for hole defects in a plate in a complex environment, and relates to a profile detection method which is realized by fusing an edge detection algorithm, heat transfer theory and deep learning. The heat transfer model is interacted with Ansys simulation software through a Python programming language and a PyAnsys library, and material attribute data is established; the deep learning model is composed of a yov5 visual recognition algorithm, and the edge detection algorithm is a Canny edge detection algorithm; firstly, an initial model of heat transfer theory is established, then a simulation model is reversely corrected through detection results of deep learning and an edge detection algorithm, and finally, the defect edge of the hole in the plate is detected. According to the method, the plate defects are detected through fusion of an edge detection algorithm and finite element heat transfer model deep learning, and the situation that hole edge detection is inaccurate due to low contrast, unclear boundary and noise area interference of a thermal image in a complex environment is avoided.

The invention relates to the technical field of intelligent construction of road engineering, and discloses an expressway asphalt pavement construction data analysis and processing system, the system performs digital modeling on an asphalt mixture and anchors the asphalt mixture to a pavement three-dimensional coordinate by creating a material state vector including a static attribute, a dynamic state and a process resume, and the system adopts a heat transfer model to construct an asphalt pavement construction data analysis and processing system. And the predicted temperature of the target vector at the future interception moment of the road roller is updated in real time and forwards simulated. Meanwhile, the system calculates a dynamic compactability window according to the compaction history and the material type, compares the predicted temperature with the window, generates a feedforward control instruction of a standard, priority or waiting operation, converts the feedforward control instruction into visual guidance, and issues the visual guidance to the vehicle-mounted terminal, so that prospective control is realized. According to the invention, the conversion of construction control from passive lag to active feedforward is realized, the fineness of the compaction process is improved, and a reliable digital quality tracing mechanism is established.

The invention discloses a power semiconductor welding layer life evaluation and failure prediction method. The method comprises the following steps: S1, acquiring chip design parameters, power semiconductor module sealing and testing process parameters and power semiconductor module working condition monitoring data; s2, constructing a heat transfer model, a mechanical stress model and an electrical property model by adopting a finite element analysis method according to the data obtained in S1; relates to the technical field of power semiconductors, and aims to comprehensively consider chip design parameters, power semiconductor module sealing and testing process parameters and working condition monitoring data, construct a plurality of physical models, deeply analyze the influence on the service life of a welding layer from multiple dimensions of heat transfer, mechanical stress and electrical performance, and greatly improve the service life of the welding layer compared with single thermal fatigue. Or by mainly depending on specific precursor parameters and utilizing a neural network fitting method, the residual life of the welding layer can be evaluated more comprehensively and accurately, and more accurate life prediction information is provided for reliable operation of the power semiconductor module.

A thermal compensation design and implementation method for near-net-shape molding of a thermoset composite part based on temperature and curing degree fields is provided. A minimum symmetric unit model is established based on sizes of the part and a mold and ambient temperature. A curing kinetic model and a heat transfer model are established. A molding temperature curve is written into a subprogram. A simulated thermal compensation is performed on a region whose temperature is lower than a molding temperature with a difference of greater than or equal to 5% of the molding temperature until the temperature difference of individual regions is less than 5% of the molding temperature. The simulated thermal compensation is then performed on those regions with a curing degree less than or equal to 0.9. A thermal compensation device is assembled according to the thermal compensation scheme for experimental verification.

The invention belongs to the technical field of computer aided design, and provides a C / C composite material thermochemical response and pore scale structure evolution prediction method, which comprises the following steps: constructing a geometric model of the pore scale of a rocket engine jet pipe; establishing a fluid flow model in a D2Q9 form; establishing a multi-component mass transport model in a D2Q5 form; establishing a heat transfer model in a D2Q5 form; establishing a heterogeneous chemical reaction model of the fluid-solid boundary; establishing an updating model of the composite material based on a volume pixel method; and based on a geometric model, a fluid flow model, a multi-component mass transport model, a heat transfer model, a heterogeneous chemical reaction model and an update model, predicting the thermochemical response and pore scale structure evolution of the C / C composite material in the rocket engine nozzle. The method is provided for studying the ablation process of the C / C composite material, and theoretical basis and guidance direction are provided for thermal performance study and fine design of the spray pipe.

The invention discloses a flexible sunlight greenhouse temperature control method, system and device and a storage medium, and relates to the technical field of greenhouse environment, the method comprises the following steps: calling a heat transfer model to carry out temperature field simulation calculation according to greenhouse internal thermal field distribution data to obtain a first predicted temperature value; calling a greenhouse temperature prediction model to perform time sequence analysis on the external environment temperature historical time sequence data to obtain a second predicted temperature value; performing data fusion according to the first predicted temperature value and the second predicted temperature value to obtain a comprehensive predicted temperature value; and according to a deviation value between the comprehensive prediction temperature value and a preset greenhouse air temperature target value, adjusting operation parameters of the greenhouse equipment. By simulating thermal field distribution in a greenhouse and processing time sequence data of an external environment, a deterministic rule and random fluctuation are covered, a deviation value with a target value is calculated according to an obtained comprehensive prediction value, and equipment is dynamically adjusted based on prediction deviation, so that ineffective energy consumption and equipment loss are reduced.